DAns, Mitochondria quantification using mitochondria staining
Sebastian Schmidt
12 Oktober, 2023
1 Loading of data
1.1 Data set
First, we load, filter, and merge the data sets.
How does the data set looks like
1.2 Set tresholds
Applied thresholds are indicated by grey horizontal line.
1.2.1 Mean_Puncta_mitoTracker_AreaShape_Area
1.2.2 Mean_Puncta_mitoTracker_Number_Object_Number
1.2.3 mitoTracker_MeanArea
1.2.4 mitoTracker_MeanCount
1.2.5 mitoTracker_MeanLength
1.2.6 Branchpoints
1.3 Counts per sample
#Apply tresholds
data <- subset(data, Mean_Puncta_mitoTracker_AreaShape_Area < 200)
data <- subset(data, Mean_Puncta_mitoTracker_Number_Object_Number < 1400)
data <- subset(data, mitoTracker_MeanArea < 0.04)
data <- subset(data, mitoTracker_MeanCount < 0.45)
data <- subset(data, mitoTracker_MeanLength < 0.03)
data <- subset(data, Branchpoints < 40)
#Save data set
write.csv(data, file = "results_Mito/tables/data_Mito.csv")Cell counts per cell line:
#data <- read.csv("results_Mito/tables/data_Mito.csv")
table(data$Metadata_SampleID)##
## i1JF-R1-018 iG3G-R1-039 i1E4-R1-003 iO3H-R1-005 i82A-R1-002 iJ2C-R1-015
## 180 487 572 363 239 362
## iM89-R1-005 iC99-R1-007 iR66-R1-007 iAY6-R1-003 iPX7-R1-001 i88H-R1-002
## 106 607 563 338 440 181Mean cell count:
mean(table(data$Metadata_SampleID))## [1] 369.83332 Visualize mitochondrial parameters
Various mitochondrial parameters are visualized for each patient-derived cell line as well as for the disease state Mean Ctrl levels are indicated by grey horizontal line.
2.1 MitoTracker Area
2.1.1 each sample
2.1.2 disease-state
2.2 MitoTracker Count
2.2.1 each sample
2.2.2 disease-state
2.3 MitoTracker Intensity
2.3.1 each sample
2.3.2 disease-state
2.4 MitoTracker Mean Area
2.4.1 each sample
2.4.2 disease-state
2.5 MitoTracker Mean Count
2.5.1 each sample
2.5.2 disease-state
2.6 MitoTracker Number Branch Ends
2.6.1 each sample
2.6.2 disease-state
2.7 MitoTracker Number Branchpoints
2.7.1 each sample
2.7.2 disease-state
2.8 MitoTracker Skeleton Length
2.8.1 each sample
2.8.2 disease-state
2.9 MitoTracker Mean Skeleton Length
2.9.1 each sample
2.9.2 disease-state
3 Statistical testing using linear mixed effects models
Nested approach (“Mitochondrial Parameter” ~ Disease_state + (1 | Disease_state:Metadata_SampleID)) to compensate for dependencies within the groups.
3.1 MitoTracker Area
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_AreaShape_Area ~ Disease_state + (1 |
## Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 41833.1 41858.6 -20912.5 41825.1 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3920 -0.6492 -0.2709 0.3061 5.8590
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 13.94 3.734
## Residual 721.25 26.856
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 39.4437 1.8016 21.893
## Disease_statesPD -0.7969 2.3663 -0.337
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.761## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_AreaShape_Area
## Chisq Df Pr(>Chisq)
## Disease_state 0.1134 1 0.73633.2 MitoTracker Count
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Number_Object_Number ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 61269.8 61295.4 -30630.9 61261.8 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8442 -0.7792 -0.1781 0.6405 3.3980
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 4150 64.42
## Residual 57375 239.53
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 363.94 29.45 12.359
## Disease_statesPD -13.22 38.60 -0.342
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Number_Object_Number
## Chisq Df Pr(>Chisq)
## Disease_state 0.1173 1 0.7323.3 MitoTracker Intensity
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -5760.7 -5735.1 2884.4 -5768.7 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7557 -0.6566 -0.0335 0.6537 5.0985
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.01182 0.1087
## Residual 0.01572 0.1254
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.333999 0.048717 6.856
## Disease_statesPD -0.003415 0.063795 -0.054
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.764## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Mean_Puncta_mitoTracker_Intensity_MeanIntensity_Corr_mitoTracker
## Chisq Df Pr(>Chisq)
## Disease_state 0.0029 1 0.95733.4 MitoTracker Mean Area
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanArea ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -30621.2 -30595.6 15314.6 -30629.2 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3575 -0.6898 -0.3120 0.3701 4.0149
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.739e-06 0.001319
## Residual 5.853e-05 0.007651
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0100208 0.0006207 16.143
## Disease_statesPD -0.0003579 0.0008147 -0.439
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanArea
## Chisq Df Pr(>Chisq)
## Disease_state 0.193 1 0.66043.5 MitoTracker Mean Count
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanCount ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -9352.0 -9326.4 4680.0 -9360.0 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.4454 -0.6933 -0.2872 0.4203 4.1937
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.0004919 0.02218
## Residual 0.0070443 0.08393
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0944784 0.0101454 9.312
## Disease_statesPD -0.0008737 0.0133005 -0.066
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.763## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanCount
## Chisq Df Pr(>Chisq)
## Disease_state 0.0043 1 0.94763.6 MitoTracker Number Branch Ends
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton ~ Disease_state +
## (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 22131.7 22157.3 -11061.8 22123.7 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0710 -0.6804 -0.3376 0.3979 5.3016
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 0.2008 0.4481
## Residual 8.5103 2.9172
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.28202 0.21351 10.688
## Disease_statesPD 0.09162 0.28034 0.327
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_NumberBranchEnds_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.1068 1 0.74383.7 MitoTracker Number Branchpoints
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: Branchpoints ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 30642.9 30668.5 -15317.5 30634.9 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1527 -0.6508 -0.3941 0.2779 4.5745
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 2.109 1.452
## Residual 57.860 7.607
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 5.5843 0.6774 8.243
## Disease_statesPD 0.3933 0.8888 0.443
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: Branchpoints
## Chisq Df Pr(>Chisq)
## Disease_state 0.1958 1 0.65813.8 MitoTracker Skeleton Length
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton ~
## Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## 42834.4 42860.0 -21413.2 42826.4 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.1404 -0.6018 -0.4357 0.2432 6.2086
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 29.46 5.427
## Residual 902.68 30.045
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 20.011 2.544 7.867
## Disease_statesPD 1.611 3.338 0.483
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: ObjectSkeleton_TotalObjectSkeletonLength_mitoTracker_Skeleton
## Chisq Df Pr(>Chisq)
## Disease_state 0.2329 1 0.62943.9 MitoTracker Mean Skeleton Length
## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## mitoTracker_MeanLength ~ Disease_state + (1 | Disease_state:Metadata_SampleID)
## Data: data
##
## AIC BIC logLik deviance df.resid
## -32577.1 -32551.5 16292.5 -32585.1 4434
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.0212 -0.6626 -0.3729 0.2750 4.4067
##
## Random effects:
## Groups Name Variance Std.Dev.
## Disease_state:Metadata_SampleID (Intercept) 1.387e-06 0.001178
## Residual 3.765e-05 0.006136
## Number of obs: 4438, groups: Disease_state:Metadata_SampleID, 12
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 0.0040882 0.0005492 7.444
## Disease_statesPD 0.0006187 0.0007205 0.859
##
## Correlation of Fixed Effects:
## (Intr)
## Diss_sttsPD -0.762## Analysis of Deviance Table (Type II Wald chisquare tests)
##
## Response: mitoTracker_MeanLength
## Chisq Df Pr(>Chisq)
## Disease_state 0.7372 1 0.3906